code: def wsd_classify(x_test, y_test=None):
      """
      classifiy target word. output all word senses ranked according to the most probable sense
  
      Args:
          x_test(numpy.ndarray): input x data for inference
          y_test: input y data for inference
  
      Returns:
           str: predicted values by the model
      """
      # test set
      x_test = np.array(x_test)
      if y_test is not None:
          y_test = np.array(y_test)
      test_set = {"X": x_test, "y": y_test}
  
      mlp_clf = MostCommonWordSense(args.epochs, args.batch_size, None)
      # load existing model
      mlp_clf.load(args.model)
  
      results = mlp_clf.get_outputs(test_set["X"])
  
      return results
  
file_path: nlp-architect/examples/most_common_word_sense/inference.py
imports: import argparse
  import logging
  import gensim
  import numpy
  from nltk.corpus import wordnet
  from termcolor import colored
  from examples.most_common_word_sense.feature_extraction import extract_synset_data, extract_features_envelope
  from examples.most_common_word_sense.prepare_data import read_inference_input_examples_file
  from nlp_architect.models.most_common_word_sense import MostCommonWordSense
  from nlp_architect.utils.io import validate_existing_filepath, check_size
label_desc: Use image node_id 1 for calling a global function with example usage: wsd_classify(x_test, y_test) and returns: results